Improved Cuckoo Search Optimization And Transductive Support Vector Machine Algorithm For E-Learning Recommendation System

Abstract

Introduction: Growing numbers of students opt for self-learning via the Internet, an established e-learning approach, as a result of the popularity and advancement of data search technology. A challenge for e-learning has constantly been the ability to learn different knowledge items methodically and effectively in a certain topic because the majority of the learning material on the network is dispersed. Still, the existing system has issue with higher error rate and computational complexity. Methods: To overcome this problem, Improved Cuckoo Search Optimization (ICSO) andTransudative Support Vector Machine (TSVM) algorithm were introduced. The main steps of this research are such as pre-processing, clustering, optimization and e- learning recommendation. Results: Initially, the pre-processing is performed utilizing K-Means Clustering (KMC) which is focused to deal with noise rates effectively. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster data where data space’s dense objectregions are examined to divide low-density areas. In the improved DBSCAN method, density reachability and density connectedness are used. Then, ICSO algorithm is applied to fine tune the parameters using best fitness values. Conclusion: Finally, the classification of recommendation system is done by using TSVM algorithm which more precise outcomes for the specified datasets. According to the findings, the recommended ICSO-TSVM approach excels the existing ones regards to higher accuracy, recall, precision, mean absolute error (MAE), and also time difficulty